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Elsevier

Adaptive Control of Complex Electromechanical Systems and Its Applications

  • 1st Edition - June 1, 2027
  • Latest edition
  • Authors: Shubo Wang, Jing Na, Xuemei Ren, Qiang Chen, Shuangyi Hu
  • Language: English

Adaptive Control of Complex Electromechanical Systems and Its Applications addresses the critical challenges of controlling nonlinear electromechanical systems that exhibit nonlin… Read more

Description

Adaptive Control of Complex Electromechanical Systems and Its Applications addresses the critical challenges of controlling nonlinear electromechanical systems that exhibit nonlinearities, parameter uncertainties, and external disturbances. This reference provides a thorough tutorial on adaptive parameter estimation, disturbance rejection, and transient performance enhancement, effectively bridging the gap between advanced theory and practical engineering applications. It offers detailed guidance on modeling non-smooth dynamics and designing robust adaptive controllers. The book benefits graduate students, researchers, and practicing engineers by presenting state-of-the-art algorithms and control strategies such as adaptive backstepping, disturbance observers, sliding mode control, and multi-motor driving systems. Supported by practical examples and experimental validation, it equips readers with the tools needed to improve control accuracy, robustness, and dynamic response in complex electromechanical systems. This resource facilitates rapid mastery of adaptive control techniques and supports innovation in research and industrial applications.

Key features

  • Presents the latest advances in adaptive control and disturbance rejection for complex electromechanical systems, enabling researchers and engineers to enhance system performance and robustness
  • Covers practical applications in robotics, servo, and motor driving systems, providing graduate students and professionals with relevant, real-world insights
  • Includes worked examples and parameter estimation methods with adaptive control design, offering a user-friendly reference that facilitates learning and efficient application in advanced control projects

Readership

Graduate students and researchers in control systems engineering

Table of contents

1. Introduction

2. Adaptive Control for Servo System with Unknown Dynamics

2.1. Adaptive Back-Stepping Control of Servo Systems with Asymmetric Dead Zone

2.2. Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction


3. Adaptive Parameter Estimation and Control for Complex Electromechanical Systems

3.1. Parameter Estimation and Adaptive Control for Complex Electromechanical Systems with Friction Compensation

3.2. Composite Learning Fixed-Time Control for Nonlinear Robotic Systems with State Constraints and Unknown Dynamics


4. Disturbance Rejection and Control for Servo Mechanisms Systems

4.1. Adaptive Dynamic Surface Control Based on Fuzzy Disturbance Observer for Servo Drive System with Elastic Coupling

4.2. Finite-Time Sliding Mode Control Based on Unknown System Dynamics Estimator for Nonlinear Robotic Systems

4.3. Finite Time Disturbance Observer Based Sliding Mode Control for PMSM with Unknown Disturbances

4.4. Fixed-Time Integral Terminal Sliding Mode Control with Super-Twisting Nonlinear Extended State Observer for Servo System with Disturbances


5. Prescribed Performance Control for Complex Electromechanical Systems

5.1. Adaptive Barrier Control for Nonlinear Servo Mechanisms With Friction Compensation

5.2. Adaptive Neural Funnel Control for Nonlinear Two-Inertia Servo Mechanisms with Backlash

5.3. Nonlinear Uncertainty Estimator Based Robust Control for PMSM Servo Mechanisms With Prescribed Performance

5.4. Adaptive Prescribed Performance Control for Nonlinear Robotic Systems


6. Multi-Motor Driving Control

6.1. Adaptive Immersion and Invariance Tracking and Synchronization Control for Multi-Motor Driving Systems with Parameter Uncertainties

6.2. RISE Based Prescribed Performance Control for Multi-Motor Driving Systems

6.3. Adaptive Predefined-Time Synchronization and Tracking Control for Multi-Motor Driving Servo Systems


7. Conclusion

Product details

  • Edition: 1
  • Latest edition
  • Published: June 1, 2027
  • Language: English

About the authors

Shubo Wang

Author

Shubo Wang received the Ph.D. degree in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 2017. From 2017 to 2024, he was with the School of Automation, Qingdao University, Qingdao, China, where he became a Full Professor in 2023. He has been with the Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, since 2024. He has coauthored one monograph and more than 70 international journal and conference papers. His current research interests include adaptive control, parameter estimation, neural network, servo system, robotic, nonlinear control, and applications for robotics and motor driving systems.

Affiliations and expertise
Professor, Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Chenggong District, Kunming City, Yunnan Province, China

Jing Na

Author
Jing Na received his B.Eng. and Ph.D. degrees from the School of Automation, Beijing Institute of Technology, Beijing, China, in 2004 and 2010, respectively. He was a Monaco/ITER Postdoctoral Fellow at the ITER Organization, Saint-Paul-lès-Durance, France, and also a Marie Curie Intra-European Fellow with the Department of Mechanical Engineering, University of Bristol, U.K. Since 2010, he has been with the Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, where he became a professor in 2013. He has co-authored one monograph and more than 100 international journal and conference papers. His current research interests include intelligent control, adaptive parameter estimation, nonlinear control.
Affiliations and expertise
Professor, Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China

Xuemei Ren

Author
Xuemei Ren received her B.S. degree from Shandong University, Shandong, China, in 1989, and M.S. and Ph.D. degrees in control engineering from the Beijing University of Aeronautics and Astronautics, Beijing, China, in 1992 and 1995, respectively. She worked at the School of Automation, Beijing Institute of Technology as a professor from 2002. She has published more than 100 academic papers. Her research interests include nonlinear systems, intelligent control, neural network control, adaptive control, multi- drive servo systems and time delay systems.
Affiliations and expertise
Professor, School of Automation, Beijing Institute of Technology, Beijing, China

Qiang Chen

Author

Qiang Chen received the B.S. degree in measure and control technology and instrumentation from Hebei Agricultural University, Baoding, China, in 2006, and the Ph.D. degree in control science and engineering from Beijing Institute of Technology, Beijing, China, in 2012. Since 2012, he has been with the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, where he was a Professor in 2022. He has published over 100 peer-reviewed papers in journals and conference proceedings. He has been authorized more than 60 invention patents, 13 of which were transferred. His research interests include adaptive control and iterative learning control with application to motion control systems.

Affiliations and expertise
Professor, Deputy Dean of the Graduate School, and Director of the Provincial Experimental Teaching Center for Electronic Information, School of Information Engineering, Zhejiang University of Technology, Xihu District, Hangzhou, Zhejiang, China

Shuangyi Hu

Author

Dr. Shuangyi Hu earned his B.S. in Automation and Ph.D. in Control Science and Engineering from Beijing Institute of Technology in 2017 and 2023, respectively. He is currently a Research Fellow at Zhejiang University of Technology. His research interests include adaptive control, repetitive learning control, and motor-drive servo systems.

Affiliations and expertise
Research Fellow, Zhejiang University of Technology, Beijing, China